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Geolocalized Modeling for Dish Recognition

NameGeolocalized Modeling for Dish Recognition

Food recognition is still not accurate enough. Most works focus on exploiting only the visual content while ignoring the context. We propose a framework incorporating discriminative classification in geolocalized settings and introduce the concept of geolocalized models, which, in our scenario, are trained locally at each restaurant location. We collected a restaurant-oriented food dataset with food images, dish tags, and restaurant-level information, such as the menu and geolocation. Experiments on this dataset show that exploiting geolocation improves around 30% the recognition performance, and geolocalized models contribute with an additional 3-8% absolute gain, while they can be trained up to five times faster.

is ieee
ieee paper year2016
price rangemedium
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